ABSTRACT
The recent studies have indicated the requisite of computed tomography scan analysis by radiologists extensively to find out the suspected patients of SARS-CoV-2 (COVID-19). The existing deep learning methods distribute one or more of the subsequent bottlenecks. Therefore, a straight forward method for detecting COVID-19 infection using real-world computed tomography scans is presented. The detection process consists of image processing techniques such as segmentation of lung parenchyma and extraction of effective texture features. The kernel-based support vector machine is employed over feature vectors for classification. The performance parameters of the proposed method are calculated and compared with the existing methodology on the same dataset. The classification results are found outperforming and the method is less probabilistic which can be further exploited for developing more realistic detection system.Copyright © 2023 Inderscience Enterprises Ltd.
ABSTRACT
During this COVID-19 outbreak, diagnostic tests were crucial in controlling its spread. This pandemic, which was caused by the SARS-CoV-2 virus, has created an urgent need for rapid diagnostic testing to enable efficient treatment and control of COVID-19. Many institutes and companies are trying to develop effective methods for detecting COVID-19. The essential indicative instrument presently utilized is reverse transcription-polymerase chain response (RT-PCR), which can have great affectability. Tragically, execution costs, time is taken, and false-negative results have limited the use of RT-PCR. Lateral flow assays (LFAs) are a type of diagnostic test that is clinically sensitive enough, might bridge various inadequacies in the present RT-PCR system, especially in low-and middle-income countries. In basic hospitals and laboratories, AuNP-LFA is a viable technique for diagnosing COVID-19, especially in emergency settings where a large number of samples must be examined quickly. This review summarizes the different diagnostic approaches for detecting the SARS-COV-2. This review thus provides guidelines for the scientists and technicians engaged in detecting SARS-COV-2. An accurate and sensitive detection system would enormously benefit in controlling this pandemic. © 2022 by the authors.
ABSTRACT
To stop the spread of COVID-19 in this outbreak, diagnostic testing is essential. Quick diagnostic tests must be employed in this pandemic, which is brought on by the severe acute respiratory syndrome (SARS)-CoV-2 virus, to successfully treat and manage COVID-19. There are several problems with the present RT-PCR system that the lateral flow assay (LFA), a kind of clinically sensitive diagnostic test, may be able to fix, especially in low-and middle-income nations. Gold nanoparticle-(AuNP-LFA) is a practical method for detecting COVID-19 in basic hospitals and laboratories, particularly in emergency situations where many samples must be quickly examined. Safe, accurate, and non-toxic diagnostic tests must be employed during the pandemic, to successfully treat and manage COVID-19. Recombinant SARS-CoV-2 nucleocapsid monoclonal antibody was employed to detect COVID-19 antigens in the presence of patients to establish a fast LFA for COVID-19. Synthesis of colloidal gold particles and antibody colloidal gold conjugates was evaluated by using UV/Vis spectroscopy. A capture line made of anti-SARS-CoV-2 antibody was coated on nitrocellulose membrane. To create the control line, goat anti-mouse IgG monoclonal antibody was coated. On a polystyrene backing board, the immunochromatographic strip was constructed in the ideal order. Using ELISA as the standard procedure, the strips' sensitivity and specificity were assessed. The results' stability and repeatability were evaluated over a 9-month period. Colloidal gold nanoparticle-based LFAs created in this study can be employed for quicker and more accurate detection of SARS-CoV-2. © 2023 Singh, et al.
ABSTRACT
Background: The literature on the impact of comorbidities on the severity and outcome of COVID-19 in kidney transplant patients is limited. We aimed to review the same. Methods: We conducted this review as per Preferred Reporting Items for Systematic Reviews and Meta-Analysis recommendations. PUBMED, Embase, Scopus, and Science Direct were searched for studies, available online till May 31, 2020. Studies reporting comorbidities, clinical course, and outcome of each kidney transplant patient with COVID-19 were included. Studies on any other organ transplant, recommendations, or review articles were excluded. The impact of comorbidities on severity and outcome was assessed. The study appraisal was done using Joanna Briggs Institute Critical Appraisal Checklist. Continuous variables were compared using Mann-Whitney U-test. Categorical variables were compared using Fisher's exact test. A univariate and multivariate logistic regression for predictors of severity and outcome, was done. P < 0.05 was considered statistically significant. The study protocol was registered with PROSPERO (CRD42020190114). Results: We analyzed 19 studies (56 patients) out of the 355 identified. The most common comorbidity was hypertension (83.92%). Nearly 30.35% of the patients had severe clinical course. The mortality rate was 19.64%. Advanced age was statistically significantly associated with severe course (P = 0.0173) and death (P = 0.0005). Men were more likely to have nonsevere course (P < 0.0001). No comorbidity had any impact on the severity or outcome. Patients with severe disease had higher odds of dying (P = 0.002). Conclusions: Comorbidities were not found to have any significant impact, hence the contribution of immunosuppression toward the severity of COVID-19 needs to be studied. Ours is the first review to assess the impact of comorbidities in kidney transplant patients with COVID-19 but limited by the number of patients.